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Reliable functionality in anomaly detection in thermal image datasets is crucial for defect detection of industrial products. Nevertheless, achieving reliable functionality is challenging, especially when datasets are image sequences captured during equipment runtime with a smooth transition from healthy to defective images. This causes contamination of healthy training data with defective samples. Anomaly detection methods based on autoencoders are susceptible to a slight violation of a clean training dataset and lead to challenging threshold determination for sample classification.
After the characteristics of the datasets and used data pipeline are introduced, the research method is discussed in detail. The approach consists of two phases. First, the proposed autoencoder models are developed and trained in the training phase. Then, in the test phase, anomaly score outputs are used for classification performance analyses.
Table 1 presents the classification performance results based on the determined T on the anomaly scores generated using the autoencoder. The classification measures indicate high performances in all the datasets, except the contaminated dataset (camera 94693). This high accuracy is due to the nature of thermal images and can be reduced depending on the texture of the input images. Nevertheless, the SSIM outperforms the MSE anomaly score in all cases.
For performance improvement, researchers combined the MSE and SSIM thresholds with KDE thresholds MSE+ and SSIM+. To conduct a comparative analysis, researchers compared the results of MSE and SSIM as the baseline approaches and further evaluated the enhanced performance achieved using the MSE+ and SSIM+ thresholds. Figure 1 displays the amount of improvement in accuracy.